Design, Modeling, and Nonlinear Model Predictive Tracking Control of a Novel Autonomous Surface Vehicle

Abstract

In this paper, we present the design, modeling, and real-time nonlinear model predictive control (NMPC) of an autonomous robotic boat. The robot is easy to manufacture, highly maneuverable, and capable of accurate trajectory tracking in both indoor and outdoor environments. In particular, a cross type four-thruster configuration is proposed for the robotic boat to produce efficient holonomic motions. The robot prototype is rapidly 3D-printed and then sealed by adhering several layers of fiberglass. To achieve accurate tracking control, we formulate an NMPC strategy for the four-controlinput boat with control input constraints, where the nonlinear dynamic model includes a Coriolis and centripetal matrix, the hydrodynamic added mass, and damping. By integrating "GPS" modules and an inertial measurement unit (IMU) into the robot, we demonstrate accurate trajectory tracking of the robotic boat along preplanned paths in both a swimming pool and a natural river. Furthermore, the code generation strategy employed in our paper yields a two order of magnitude improvement in the run time of the NMPC algorithm compared to similar systems. The robot is designed to form the basis for surface swarm robotics testbeds, on which collective algorithms for surface transportation and self-assembly of dynamic floating infrastructures can be assessed.

Modular Fluidic Propulsion Robots

“…Wang et al [40] introduce the Roboat, a modular robotic boat with a force-efficient thruster configuration. The modules can move and reconfigure in 2-D, thereby dynamically forming floating structures.…”

Model Predictive Super-Twisting Sliding Mode Control for an Autonomous Surface Vehicle

“…Also, in accordance to combination of the equations (5), (6) and 7, the first derivative of sliding surface can be obtained as (15) Let us present the cost function as follows:…”

“…In [5], a back-stepping control approach combined with sliding mode control algorithm was designed to cover the trajectory tracking problem of an under-actuated USV. An autonomous robotic boat was presented in [6] and a nonlinear model predictive control (NMPC) was adopted for its controller. However, the uncertain nonlinearities, resulting from the changes in inertia and drag matrices of the robotic boat, were not taken into account there.…”

Robust-adaptive dynamic programming-based time-delay control of autonomous ships under stochastic disturbances using an actor-critic learning algorithm

“…In [10], a backstepping control approach combined with sliding mode control algorithm was designed to address the trajectory tracking problem of an underactuated USV. An autonomous robotic boat was presented in [11], and for its controller, a nonlinear model predictive control (NMPC) was adopted. However, the uncertainty effect, resulting from the changes in inertia, was not taken into account there.…”